Creating short-term stockmarket trading strategies using Artificial Neural Networks: A Case Study

Bruce J. Vanstone, Bond University
Tobias Hahn, Bond University

Article comments

Presented at the 2008 International Conference of Computational Intelligence and Intelligent Systems (ICIIS), World Conference on Engineering (WCE 2008), London 2nd - 4th July, 2008

This paper won the Merit Award for the ICIIS 2008 conference.

Abstract

Developing short-term stockmarket trading systems is a complex process, as there is a great deal of random noise present in the time series data of individual securities. The primary difficulty in training neural networks to identify return expectations is to find variables to help identify the signal present in the data. In this paper, the authors follow the previously published Vanstone and Finnie methodology. They develop a successful neural network, and demonstrate its effectiveness as the core element of a financially viable trading system.